3D system including rendering with eye displacement

ABSTRACT

A three dimensional system including rendering with eye displacement.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional App. No.62/255,132, filed Nov. 13, 2015.

BACKGROUND OF THE INVENTION

Two dimensional video content, such as obtained with a video camerahaving a single aperture, is often either projected onto a displayscreen for viewing or viewed on a display designed for presenting twodimensional content. Over time, the resolution of displays has tended toincrease, from standard television interlaced content resolution (e.g.,480i), to high definition television content (e.g., 1080i), to 4Kdefinition television content (4K UHD), and even to even higherdefinition television content (e.g., 8K UHD). Such increases in videoresolution technology only provide for limited increases in the apparentimage entertainment to the viewer. Accordingly, the viewer is onlyimmersed in the video experience to a limited extent.

To increase the immersive experience of the viewer it is desirable toeffectively convert two dimensional image content into three dimensional(3D) image content, including glasses-free and glasses-based threedimensional content, which is thereafter displayed on a suitable displayfor viewing three dimensional image content. The perception of threedimensional content may involve a third dimension of depth, which may beperceived in a form of binocular disparity by the human visual system.Since the left and the right eyes of the viewer are at differentpositions, each eye perceives a slightly different view of a field ofview. The human brain may then reconstruct the depth information fromthese different views to perceive a three dimensional view. To emulatethis phenomenon, a three dimensional display may display two or moreslightly different images of each scene in a manner that presents eachof the views to a different eye of the viewer. A variety of differentdisplay technologies may be used, such as for example, anaglyph threedimensional system, passive-polarized three dimensional display system,active-shutter three dimensional display system, autostereoscopiclenticular glasses-free 3D display system, autostereoscopicparallax-barrier glasses-free 3D display system, and head mountedstereoscopic display system.

As three dimensional display systems become more readily prevalent thedesire for suitable three dimensional content to present on suchdisplays increases. One way to generate three dimensional content isusing three dimensional computer generated graphics. While such contentis suitable for being displayed, the amount of desirable such threedimensional computer generated content is limited and typically used foranimated content. Another way to generate there dimensional content isusing three dimensional video camera systems. Likewise, while such videocamera content is suitable for being displayed, the amount of desirablesuch three dimensional content is likewise limited. A preferabletechnique to generate three dimensional content is using the vastamounts of available two dimensional content and converting the twodimensional content into three dimensional content. While suchconversion of two dimensional content (2D) to three dimensional content(3D) conversation is desirable, the techniques are conventionallycomplicated and labor intensive.

The foregoing and other objectives, features, and advantages of theinvention may be more readily understood upon consideration of thefollowing detailed description of the invention, taken in conjunctionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates an exemplary two dimension to three dimension imageconversion process.

FIG. 2 illustrates an exemplary 2D to 3D image conversion system.

FIG. 3 illustrates an exemplary neural network.

FIG. 4 illustrates inputs to the neural network.

FIG. 5 illustrates a selection of image based inputs to the neuralnetwork and the outputs thereof.

FIG. 6 illustrates a selection of bit depths associated with a threedimensional image.

FIG. 7 illustrates selection of pixels of an image shifted differentdistances to provide right eye versus left eye displacements derivedfrom estimated depth to create the perception of apparent threedimensional image depths.

FIG. 8 illustrates a screen place and a depth space “D”.

FIG. 9 illustrates a screen plane, a shift “Z”, a total shifted depth,and a resulting shifted depth.

FIG. 10 illustrates a corresponding left eye displacement view and aright eye displacement view at a first depth plane shifted to a secondbit depth in front of the screen plane.

FIG. 11 illustrates a left eye displacement and a right eye displacementat a first depth plane shifted to a second bit depth in front of thescreen plane using a non-linear mapping.

FIG. 12 illustrates a left eye and a right eye at a first depth planeshifted to a second pixel depth in front of the screen plane using aplurality of non-linear mappings.

FIG. 13 illustrates a depth engine, a depth reprofiling modificationmapping, and a rendering engine.

FIG. 14 illustrates a video stream processing technique.

FIG. 15 illustrates a left-eye image queue and a right-eye image queue.

FIG. 16 illustrates a left image queue and a right image queue receivinga sequence of displaced pixel values.

FIG. 17 illustrates a display with pixels and/or sub-pixels and anoptical lens element for supporting lenticular glasses-free 3Dautostereoscopic multi-view.

FIG. 18 illustrates a lenticular type imaging arrangement.

FIG. 19 illustrates a lenticular type sub-pixels under the slantedlenticular lens imaging arrangement.

FIG. 20 illustrates an alternate model for computing pixel displacementwith examples of a pixel depth behind the screen and a pixel depth infront of the screen.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENT

One technique to achieve two dimensional (2D) to three dimensional (3D)conversion is using a modified time difference technique. The modifiedtime difference technique converts 2D images to 3D images by selectingimages that would be a stereo-pair according to the detected motions ofobjects in the input sequential images. This technique may, if desired,be based upon motion vector information available in the video orotherwise determined.

Another technique to achieve two dimensional (2D) to three dimensional(3D) conversion is using a computed image depth technique. The 3D imagesare generated based upon the characteristics of each 2D image. Thecharacteristics of the image that may be used, include, but are notlimited to for example, the contrast of different regions of the image,the sharpness of different regions of the image, the chrominance ofdifferent regions of the image, and texture of different regions of theimage. Alternatively, the hue, the saturation, the brightness, and thetexture may be used. The sharpness, contrast, and chrominance values ofeach area of the input image may be determined. The sharpness relates tothe high frequency content of the luminance signal of the input image.The contrast relates to a medium frequency content of the luminancesignal of the input image. The chrominance relates the hue and the tonecontent of the color signal of the input image. Adjacent areas that haveclose color may be grouped together according to their chrominancevalues. The image depth may be computed using these characteristicsand/or other characteristics, as desired. For example, generally nearpositioned objects have higher sharpness and higher contrast than farpositioned objects and the background image. Thus, the sharpness andcontrast may be inversely proportional to the distance. These values maylikewise be weighted based upon their spatial location within the image.Other techniques may likewise be used to achieve a 2D to 3D conversionof an input image, including motion compensation, if desired. Referringto FIG. 1, with a suitable depth map from the 2D to 3D conversionprocess, a 3D image generation process may be used to generate the 3Dimages based upon the image depth map.

Completely automatic 2D to 3D conversion processes typically result insub-optimal three dimensional image for post-production contentconversion. Post-production content conversion is typically very laborintensive with stereographers creating hand painted depth maps andselecting objects that move and rotoscoping to copy those edits to asmany following frames as possible.

Referring to FIG. 2, the video content may be stored on a storage system120, available from a network 150, or otherwise, and processed by thecomputing system 110. The user may use a display 100 as a user interface160 for selecting three dimensional control parameters for the videocontent. The control parameters may be used to modify the 2D to 3Dconversion process. The computing system may provide the 2D videocontent and/or control parameters for the 2D to 3D conversionaccelerator, as described in detail later. The 2D-3D conversionaccelerator then processes the 2D video content, based at least in parton the control parameters provided (if any), to generate 3D videocontent. Preferably the 2D video is provided together with the controlparameters from the computing system 110 to the conversion accelerators.For example, (1) the video content may be provided as a single videostream where the left and right images are contained in a single videostream, and/or (2) the video content may be provided as two separatevideo streams with a full video stream for the left eye's content and afull video stream for the right eye's content. The 3D video content, asa result of the conversion accelerator, is rendered on the threedimensional display 140 so that the user may observe the effects of thecontrol parameters in combination with the 2D to 3D conversionaccelerator. The user may modify the control parameters, such as bymodifying selections on the user interface, for the video content untilsuitable 3D images are rendered on the three dimensional display 140.The resulting three dimensional content from the 2D-3D conversionaccelerator may be provided to the computing system 110, which may bestored in a three dimensional video format (e.g., 3D side-by-side, 3Dframe-pack, frame-sequential 3D, for subsequent rendering on a threedimensional display. The 2D-3D conversion accelerator is preferably anexternal converter to the computing system 110.

While a user assisted conversion from 2D image content to 3D imagecontent is feasible, it tends to be rather cumbersome to convert asubstantial amount of such video content. Accordingly, it is desirablein a 3D entertainment device to include a fully automated 2D imagecontent to 3D image content conversion system that provides a highquality output. Typically conversion systems are based upon combiningvisual analyzing and combining cues to create a depth map of the 2Dimage. The depth map contains a depth value for each pixel in the imageor video frame. Rather than design increasingly more complicated 2D to3D mathematical models of the 2D image content depth estimation, adifferent paradigm is being applied. In particular, the differentparadigm should not be based upon attempting to determine a mathematicalmodel and/or algorithmic based approach to analyze the 2D image content.A different paradigm preferably includes a neural network, which is aninformation processing paradigm that is inspired by the way biologicalnervous systems process information. In this way the neural networkbrain can be trained to create high quality image depth maps that aremore extreme and approximate or mimic what a human could do. Thetraining can result in conversions that are much more complex andsophisticated than a human team might be able to invent manually. Thelonger you train it the better it gets. Once trained, the neural-netbrain with its weighted synapses of each modeled neuron and otherlearned parameters can be copied on to a hardware board or microchip andput into consumer or other market devices. These devices might just copythe neural-net, or they might also include on-board training processessuch as genetic or back-propagation learning technology to continuallyimprove themselves.

The result of the 2D to 3D conversion of images using the neuralnetworks results in a depth estimation of each pixel in an image alongwith the 2D source image that are then processed using a 3D image renderprocess. It is to be understood that any 3D display technology may beused, such as for example, stereo 3D display and multi-view autostereoscopic display, or even holographic display. The system mayprocess all of the input frames in order or a sub-set thereof. Therendered images may be suitable for glasses-based 3D or glasses-free 3Dviewing technologies. The display may also be a projected display, ifdesired.

The result of the conventional 3D image rendering process tends toresult in limited pop-out of the image content from the surface of thedisplay particularly for glasses-free 3D displays due to limitations ofthe optics. This limits the compelling nature of the glasses-freedisplay experience. Typically, if the 3D depth and pop-out is pushed toa more extreme level, artifacts (errors) in the 2D to 3D conversionprocess tend to become pronounced, so the 3D experience is limited inmany displays.

Referring to FIG. 3, the neural network includes a number ofinterconnected computational elements working cooperatively to solve aproblem. The neural network may be generally presented as a system ofinterconnected neurons which can compute values from inputs, and may becapable of learning using an adaptive technique, if desired. In general,the neural network may include the following characteristics. First, itmay include sets of adaptive weights, e.g., numerical parameters thatare tuned by a learning process. Second, the sets of adaptive weightsmay be capable of approximating a wide range of functions of theirinputs. The adaptive weights, threshold activation functions may beconceptually considered the connection strengths/function computation onsynapses between neurons. Traditionally, activation functions have beenimplemented with some sort of analog circuit due to their complexity.Preferably, a variety of synapse specific transfer function models maybe implemented using a combined math-function and table-driven function.Preferably, synapse transfer function shapes can also be modified byneural training. Being able to modify the transfer function increasesthe sophistication of computation that can be performed at a synapse andthereby improves the intelligence of the neural net with less neurons.In general, the neural network, thresholds, and transfer functionsperform many functions in collectively and in parallel by units. Inaddition, the neural network may optionally include back propagation,feed forward, recurrent, and genetic learning structures. The neuralnetwork technique can achieve a natural appearance for 3D structuressimilar to what a human might do manually because it can learn bycomparing its results with human optimized examples.

Referring to FIG. 4, the first layer is the inputs to the neural networkwhich may be the output from various pre-analyzers including color spaceconversion, resolution decimation, texture, edges, facial and objectdetection, etc. The pixel values may be converted to a different format,if desired. Each of the neuron synapses may have a various associatedweights, thresholds, and transfer functions associated therewith. Eachactivation function may be updated and may be unique for each node orsynapse.

Referring to FIG. 5, the preferable inputs to the neural network includeinformation that may characterize the image. One of the inputs for animage, or regions of an image thereof, are the values of the pixels andthe color components thereof. In many cases, the color componentsthereof are red, blue, green, and the associated magnitudes of the red,blue, green. Other techniques may be used to characterize an image, suchas for example, red-blue-green-yellow, hue-saturation-brightness, orYCrCb.

While the hue, saturation, and/or brightness provide informationregarding the color characteristics of the image, it is also desirableto include information related to the nature of the texture of theimage. In general, texture characteristics quantify the perceivedtexture of an image. As such, texture characteristics provideinformation about the spatial arrangement of color and/or intensities inan image or a selected region of the image. Texture provides indicationsthat an object in an image or frame might be closer. A texture may haveits own 3D depth texture.

While the hue, saturation, and/or intensity, together with texturecharacteristics, provides information regarding the characteristics ofthe image, it is desirable to also have information regarding the edgecharacteristics of the image. In one manner, edges may be determined atpoint or lines or arches of an image at which the image brightnesschanges sufficiently sharply. The edge aspects of the image tend toindicate discontinuities in the depth of the image, discontinuities inthe surface orientation, changes in material properties, and/orvariations in scene illumination.

It may be desirable to include information related to the structure ofitems within the image. Such structure information may be obtained in asuitable manner, such as through segmentation based techniques. Ingeneral, the structural information may be generally related to theidentification of items within the image. This structural informationmay be provided as an input to the neural network to further determine amore accurate depth map.

It may be desirable to identify facial images within the image. Inaddition, it may be desirable to further identify facial features withinthe facial images. The facial features of the image tend to be thoseregions of the image that are of particular importance to the viewer. Inaddition, it is desirable to limit the three dimensional aspects of thefacial region of the image so that it doesn't inadvertently becomedistorted. In addition, it is desirable to modify the depth map so thatthe facial features will tend to be rendered in a visually pleasingmanner. Accordingly, the rendering for the facial features may bedifferent than that for other aspects of the image.

It may be desirable to modify the estimation of the depths and/or therendering based upon the type of rendering device. The estimation of thedepth and/or the rendering may also be based upon updating of the fieldsand/or system feedback.

One technique for training a neural network is to collect a selection ofimages and associated instrument measured three dimensional depth maps.The output of the processing by the neural network may be graded foraccuracy, and the neural network updated accordingly to cause learning.

Referring to FIG. 6, with an improved depth map, with a reduced amountof errors or other irregularities, it is desirable to increase the 3Ddepth and popout for the three dimensional image or frame on thedisplay. For example, for an eight bit depth range for the depth mapand/or the 3D image generation process, the depth behind the image planemay be broken up into a depth having 256 depths (e.g., 8 bits). By wayof example, the 8-bit depth map may be referenced from a 255 level beingat the plane of the screen. In this manner, all of the three dimensionalcontent would appear to be behind the screen. Pretty much all 3D depthmay be represented by a range of 0 to 255 or eight bits of resolution.The amount of perceived depth is determined by the amount of horizontaldisplacement of left and right eye pixels associated with a depth value.One can think of 3D as a three dimensional box where the top, bottom,left and right sides are at the edges of the display. The far back ofthe box is at depth 256 and the near point is at the display screen andhas depth value of zero. In this example all 3D is rendered behind thedisplay screen. If you consider three dimensional coordinates where xaxis is across the width of the screen, and y axis measures up and downon the screen, then the z axis measures distance behind the screen or infront of the screen. There may be an additional control of a z axisoffset control where the three dimensional box can be offset on the zaxis to be partly or even entirely in front of the screen instead ofonly behind the screen. By offsetting the three dimensional box partlyout of the screen creates the 3D popout effect that so many viewersassociate with the pleasure of watching 3D. The content moves into theviewer's geometric space. By using this z offset, content that isoriginally in 2D can be converted and spread across the space in frontof the screen and behind the screen. While content is currentlyconverted by humans in a very manual intensive process to create thiseffect in movies, this adjustment technique may do this in a real-time2D to 3D converter. The movie “Titanic” was converted to 3D by a team of300 people and took 18 months. The technique described herein mayconvert 2D “Titanic” to 3D real-time in less than one frame delay (onesixtieth of a second) and have part of the movie significantly poppedout into the viewer's space during the entire movie in a naturaleasy-to-watch way that creates an enjoyable 3D experience. The techniquecan do that and output to any type of 3D display that is glasses-based3D, or glasses-free 3D, or even holographic 3D.

Referring to FIG. 7, for example a pixel in the picture plane with adepth map pixel corresponding to depth-level 128 may be viewed at such adepth by shifting the pixel for the right eye view to the right by anappropriate distance and shifting the left eye view to the left by anappropriate distance from what would have otherwise been a centrallocation in a two dimensional image. The same pixel in the picture planewith a depth map corresponding to 64 may be viewed at such a depth byshifting the pixel in the right eye view to the right by an appropriatedistance and shifting the left eye view to the right by an appropriatedistance from what would have otherwise been a central location in a twodimensional image. As illustrated in FIG. 7, the central location wouldbe the same for both shifts, namely, a bit depth of 128 and a bit depthof 64. As it may be observed, the greater that the pixel position ishorizontally separated in space, one for the left image and one for theright image, the greater the apparent depth of the pixel in the image.

Referring to FIG. 8, the image may be mapped into a depth space basedupon a relative location of the front of the screen, which may beconsidered a “0” point for convenience having a depth of “D”, such as256 levels for an 8-bit depth. It may be desirable to provide theappearance of a substantial portion of the 3D image appearing in frontof the plane of the screen for increased visual desirability.

Referring to FIG. 9, the depth map of the image may be shifted by anamount “Z” relative to the screen plane. In this manner, the maximumdepth of the image behind the screen plane is reduced by the amount Z.In this manner, the depth of the image in front of the screen plane isincreased by the amount Z. As it may be observed, the overall depth ofthe pixel remains the same. In other embodiments, the image may bescaled to shift the image to increase the overall depth of the image infront of the screen plane, such as using a linear or non-linear functionZ. Also, the image may be both scaled and shifted, if desired. However,preferably the resulting shifted depth of a pixel is less than the totalshifted depth of the image. The shifting of the pixel may be achieved byadding and/or subtracting a depth value of Z and then remapping thepixels to the modified three dimensional depth.

Referring to FIG. 10, by way of example, an original bit depth of 128may include a pair of shifted pixels to provide such a bit depthappearance. If the offset of Z is −200, then the resulting bit depth ofthe pair of shifted pixels will be −72 (i.e., 128−200=−72). Negative zaxis values are in front of the display screen, it is observed, in theprocess of shifting a pixel across the screen plane, the direction ofthe pixel shifts for the eyes swaps when the pixel z position has anegative value. The pixel for the right eye is shifted to the left, andthe pixel for the left eye is shifted to the right eye. This process maybe repeated for all the pixels of the image based upon their respectivedepth value and the three dimensional box z offset.

In many cases, the spatial separation between objects is not welldefined. This lack of spatial separation between objects tends to resultin difficulty in the discrimination of objects in three dimensionalspace. After further consideration, it was determined that thenon-linear nature of the human visual system results, at least in part,in such difficulty. In particular, as objects get closer to the viewerthe ability of the human visual system to discriminate between differentobjects is reduced. To reduce the lack of spatial separation betweenobject in the image, especially as a result of modification of themapping of the objects to spread the across depth, it is desirable toinclude a non-linear re-mapping process. Referring to FIG. 11, amodified mapping may be based upon a generally concave curve-likefunction whereas the pixel mapping increasingly moves further in frontof the display the curve tends to displace the pixels a greaterdistance. This revised mapping may be used for the entire display or aportion thereof.

Referring to FIG. 12, the image may be separated into a plurality ofdifferent regions, such as region 1, region 2, and region 3. The regionsare preferably defined based upon the objects detected in the image,such as for example, using a segmentation based technique, a face basedtechnique, a texture based technique, etc . . . One of the regions, forexample, may be a facial region of a person. For each of the regions, adifferent mapping may be used that is selected to enhanced the visualquality for the viewer.

Referring to FIG. 13, the 2D to 3D conversion of images (e.g., depthengine) may result in a pixel depth estimation. The data structure mayprovide a mapping between the input depth map and the output depth map,which accounts for the non-linear optimization of the depth of theimage. The optimized depth map is then provided to the 3D image renderprocess (e.g., rendering engine). More than one data structure may beused, if desired, each with different properties. This provides anefficient technique for the mapping for the depth map adjustment. By wayof example, the depth map re-profiling may be performed in accordancewith a look-up-table. Each table entry may re-map an input depth valueto a modified output value. By way of example, the depth mapre-profiling may be performed in accordance with a formula. Each inputdepth value may be modified to a modified output value based upon theformula. By way of example, the depth map re-profiling may be performedin accordance with a non-linear curve (e.g., a mapping between an inputdepth N and an output depth Y). Each input depth value may be modifiedto a modified output value based upon the curve. By way of example, thedepth map re-profiling may be performed in accordance with a linearlevel (e.g., a linear mapping between an input depth N and an outputdepth Y). Each input depth value may be modified to a modified outputvalue based upon the level. By way of example, the depth mapre-profiling may be performed in accordance with a histogram basedtechnique (e.g., a histogram of values where the lower point may bedragged to stretch the depth towards or away from the back, where thehigher point may be dragged to stretch the depth towards or away fromthe front, and a central point to stretch or compress the depth forwardor backward). Each input depth value may be modified to a modifiedoutput value based upon the histogram.

Display devices tend to include a substantial number of pixels, such asa 4K display having 4096×2160 for an iMax movie, or 3840×2160 for new 4KUHD TV standard. An 8-bit per color channel 4K UHD video frame requiresa buffer memory having a size of approximately 32 MB to store anuncompressed frame of data. Using such a large buffer for one or moreframes for the neural network tends to be costly and consume significantamounts of power in memory accesses which is problematic for a mobiledevice which has limited battery life.

Referring to FIG. 14, to reduce the power consumption requirements it ispreferable to receive a compressed video bitstream serially, the way itis broadcast, in a line-by-line manner, then uncompress it serially in aline-by-line manner. The uncompressed video bitstream is then providedto the depth engine in a line-by-line manner (or portions thereof). Inthis manner, the depth engine outputs a depth map in a line-by-linemanner (or portions thereof). The depth engine may include a limitedamount of temporal buffering so that small regions of the image may beprocessed to determine image characteristics, such as texture, edges,facial regions, etc. In this manner, a few lines of pixels of aparticular image (i.e., less than all) are being provided to the depthengine while the depth engine is simultaneously providing its outputs,which are likewise being provided to the rendering engine while pixelsof the particular image are still being provided to the depth engine.This technique substantially reduces the buffering requirements, andtherefore the power consumption of devices, such as mobile devices. Thisis not readily achieved by processor-based/software-based systems do tothe limited performance of processes, but it is more readily achievablewith a neuro network architecture.

One technique to modify the bit depth of the depth map may be asfollows. The system may use a small direct memory access memory such asa 256 deep memory where the original depth value is used as an index(address) into the memory which outputs a new depth value.

Referring to FIG. 15, the depth engine or the modified depth map, may beprovided to a FIFO queue of streaming pixels for the left image view anda FIFO of pixels for the right image view that is provided to therendering engine. In some embodiments, the queues may be a combinedqueue, if desired. The queue is preferably sized to be representative ofat least the largest potential displacement plus and minus permitted ofa corresponding pair of pixels. The source pixel is displaced from themiddle of the fifo based upon the displacement associated with the thepixel's depth map value and z offset control and the specific view.Additional “video effects” displacement offsets can be added to thenormal displacement offset to create a variety of special effects orvideo compensations for the image on specific display technologies.

With the displacement of the pixels being known for a particularlocation of the image, the right pixel may be displaced in the rightimage queue buffer at an approximate position relative to the left pixelqueue buffer. For each pixel of the image being provided for a line orportion thereof for the image, the pixel values may be positioned in anappropriate location within the respective left image queue and theright image queue. Because the pixels are handled real-time as they flowthrough the architecture, there is no need for the typical external DRAMto buffer many tens or hundreds of frames that would be needed if thiswas implemented in software. This dramatically reduces power, and diesize because the structure is orders of magnitude more efficient than aprocessor or array of processors.

Referring to FIG. 16, an embodiment illustrates one technique to use adisplacement technique with a pair of buffers. This particular exampleis a stereo or two-eye view, but may also be modified for a glasses-free3D model when more views are desired. In that case, there would often bea row for each view. The depth map or modified depth map may have apixel value A with a displacement D1. The pixel value A is then includedin the left image queue to a pixel position that is left of the originaland pixel value A is inserted in the the right image queue to a pixelposition that is right of the original by an amount corresponding to D1.This is a stereo 3D example or 2-view autostereo example. For multi-viewthere would be a unique displacement in each FIFO that represents amulti-view view. In the Stereo 3D or two-view example, the pixel value Bis then included in the left image queue and the right image queueoffset from the mid-point with a displacement D2. The pixel values of Aand B are shifted to the right, the depth map or modified depth map mayhave a next pixel value of C with a displacement D3. The pixel value Cis then included in the left image queue and the right image queueoffset from the mid-point with a displacement D3. The pixel values of A,B, and C are shifted to the right, the depth map or modified depth mapmay have a next pixel value of D with a displacement D4. The pixel valueD is then included in the left image queue and the right image queueoffset from the mid-point with a displacement D4. The pixel values of A,B, C, and D are shifted to the right, the depth map or modified depthmap may have a next pixel value of E with a displacement D5. The pixelvalue E is then included in the left image queue and the right imagequeue offset from the mid-point with a displacement D5. The pixel valueof A is provided for the left image and the pixel value of D is providedto the right image. This process may be continued, as desired. As it maybe observed, the size of the buffer may be substantially reduced thussaving power consumption for the device. As it may be observed, theplacement of the pixel values in the queues are performed with onlywrite commands which is more energy efficient, and the only a pair ofpixel values are ready out of the queue for rendering on the display.

To help understand and example of how glasses-free 3D displays work,referring to FIG. 17, in a typical lenticular autostereoscopic displayapparatus 1700 includes a matrix pixel display device comprising a LC(liquid crystal) display panel 1710 having a row and an angular lenscolumn array of display elements 1720 and acting as a spatial lightrefraction to visually isolate specific views relative to each of aviewer's eyes a backlight 1730 is also illustrated. Lenticular elementsare provided such as by using a lenticular sheet optical lens withprisms 1740 whose lenticules 1750 (exaggerated in size), includeelongate semi-cylindrical lens elements, extend in the column directionof the display panel, parallel to the display element columns. Eachlenticule overlying a respective group of two, or more, adjacent columnsof display elements. In many LCD display panels the LCD matrix includesregularly spaced rows and columns of display elements. Typically, thedisplay arrangements are arranged as columns of approximately squarepixels, where each pixel is composed of a row of red, green, and bluesub-pixels. A group of three of or more sub-pixels (e.g., red, green,and blue) form a pixel of the display. Other structures and arrangementsof display elements and optical elements may be used.

In an arrangement where each lenticule is typically associated with twoto four columns of display sub-pixels per pixel row, the displaysub-pixels in each column provide a vertical segment of a specificeye-view to be rendered. A single prism on the lenticular lens typicallyhas a magnification of 2× to 4× which allows primarily one of thesubpixels to be seen from a specific eye-view angle on a specific pixelrow. Being that a viewer second eye is at a different horizontal viewingposition, it would see a different view and subpixel compared the thefirst eye. This is what enables the ability to deliver a different viewexperience to each eye. In multi-view screens which have 7, 8 or 9views, a viewer can move their head side to side and see various viewsin each eye that appear like you can see around 3D objects.

Referring to FIG. 18, the operation of a lenticular type of an imagingarrangement is illustrated. The light source, display panel, andlenticular sheet are illustrated. The arrangement provides three viewsof each image projected in a different direction. Eye position 1 couldbe the viewer's right eye, Eye position 2 could be a viewer's left eye.Each sub-pixel on a pixel row of the display is driven with informationfor one specific view, such as for the left or right eye of the viewer.Given that each eye of a view sees a different view, a person willperceive a stereoscopic image. It may be observed that the particularview being observed depends on the location of the viewer, which isrepresented at a particular observing location.

Referring to FIG. 19, the lenticules may be arranged in a slantedarrangement with respect to the columns of display pixels, that is,their main longitudinal axis is at an angle to the column directions ofthe display element array. In this arrangement, the sub-pixels arelabeled with their corresponding view of the multi-view arrangement. Asit may be observed, some of the pixels are split among a plurality ofdifferent lenticules so that part of its light is projected by more thanone lenticular. In addition, the particular view being observed dependson the location of the viewer, which may be represented as a particularpoint location.

Referring to FIG. 20, another technique of calculating the horizontalpixel displacements is illustrated with three different pixel depths, apixel at a depth position behind the screen versus a couple pixelspositions in front of the screen. Unlike the previous technique wheredepth and popout are independent of a viewers somewhat independent of aviewers distance from the display screen, this modified technique offerspopout and depth which are proportionally relative to the viewersdistance from the display screen. This improves the 3D experience for aviewer that is further from the display screen.

With a pair of eyes of a viewer at a given distance apart and at a givendistance from the screen, the displacement of the pixels displayed onthe screen may be illustrated as S1. However, when the pixel isillustrated at a depth in front of the screen, the pixel shifts reversefor the eyes and the shift of the pixels for being displayed on thescreen may be illustrated as S2. As it may be observed, the distance ofthe shift on the screen varies with the depth behind the display and thedepth in front of the display. In addition, the shift in the pixeldistances should be based upon the distance between the eyes of theviewer. In this manner, it may be observed that with increasing depthbehind the display the shift behind S1 tends to vary from thedisplacement being substantially equal to the distance between the eyesof the viewer (at a distance behind the display nearing infinity) to adisplacement of zero with the distance at the display. In this manner,it may be observed that with increasing depth in front of the displaythe displacement in front S2 tends to vary from the displacement beingzero with the distance in front of the display being equal to zero to asubstantial displacement that increases substantially as the shift getsincreasingly closer to the viewing plane. It may be observed, that theshift behind S1 for changes in depth behind the display results inrelatively minor shifts compared to the corresponding shifts S2 forchanges in the depth in front of the display. Accordingly, the depth mapand/or rendering should account for the differences in the renderingwith respect to the distance between the eyes of the viewer and thedistance that the viewer is from the display screen. This creates a yetmore realistic 3D geometries and can facilitate a greater 3D pop-outeffect in front of the display. Moreover, with increasingly greaterchange in the depth in front of the display, the shift in front S3 tendsto vary at an ever increasing manner such that even a minor z axisoffset in front of the display results in a substantial displacement ofthe pixels.

The terms and expressions which have been employed in the foregoingspecification are used therein as terms of description and not oflimitation, and there is no intention, in the use of such terms andexpressions, of excluding equivalents of the features shown anddescribed or portions thereof, it being recognized that the scope of theinvention is defined and limited only by the claims which follow.

The invention claimed is:
 1. A method for conversion of a series of two dimensional images using a processor into a series of three dimensional images comprising: (a) said processor receiving said series of two dimensional images where each of said series of two dimensional images are from a same viewpoint, where said series of two dimensional images do not include any images from a viewpoint other than said same viewpoint; (b) said processor processing said series of two dimensional images all of which are from said same viewpoint and do not include any images from a viewpoint other than said same viewpoint to determine a respective depth map associated with each pixel or clusters of pixels of said series of two dimensional images where all of said series of two dimensional images are from said same viewpoint, where said series of two dimensional images do not include any images from a viewpoint other than said same viewpoint; (c) said processor processing said depth map depths to create corresponding source pixel horizontal displacements for each eye-view to be rendered for a target glasses-based or glasses-free 3D or holographic display; (d) said processor displacing the pixels to create a fully or partially populated image for each view, wherein portions of said series of three dimensional images has an appearance of being behind a plane of a screen of said display and other portions of said series of three dimensional images has the appearance of being in front of said plane of said screen of said display, where a first depth of a first pixel of said depth map being a first distance behind of said plane of said display screen results in a first displacement of said pixel on said display, where a second depth of a second pixel of said depth map being a second distance in front of said plane of said screen of said display results in a second displacement of said pixel on said display, wherein said first depth is the same as said second depth from said plane of said screen, where said second displacement is greater than said first displacement; (e) rendering said three dimensional images based upon said two dimensional images all of which are from the same viewpoint and do not include any images from a viewpoint other than said same viewpoint on a display; and (f) wherein as said second depth becomes increasingly larger, wherein a second shift becomes increasingly larger in a non-linear manner. 